SoroushVahidi/scidocs huggingface dataset:
Connecting Language and Knowledge Bases with Embedding Models for Relation Extraction
(2013)

This paper proposes a novel approach for relation extraction from free text which is trained to jointly use information from the text and from existing knowledge. Our model is based on two scoring functions that operate by learning low-dimensional embeddings of words and of entities and relationships from a knowledge base. We empirically show on New York Times articles aligned with Freebase relations that our approach is able to efficiently use the extra information provided by a large subset of Freebase data (4M entities, 23k relationships) to improve over existing methods that rely on text features alone.
VisKE: Visual knowledge extraction and question answering by visual verification of relation phrases
(2015)

How can we know whether a statement about our world is valid. For example, given a relationship between a pair of entities e.g., `eat(horse, hay)', how can we know whether this relationship is true or false in general. Gathering such knowledge about entities and their relationships is one of the fundamental challenges in knowledge extraction. Most previous works on knowledge extraction have focused purely on text-driven reasoning for verifying relation phrases. In this work, we introduce the problem of visual verification of relation phrases and developed a Visual Knowledge Extraction system called VisKE. Given a verb-based relation phrase between common nouns, our approach assess its validity by jointly analyzing over text and images and reasoning about the spatial consistency of the relative configurations of the entities and the relation involved. Our approach involves no explicit human supervision thereby enabling large-scale analysis. Using our approach, we have already verified over 12000 relation phrases. Our approach has been used to not only enrich existing textual knowledge bases by improving their recall, but also augment open-domain question-answer reasoning.
Learning Structured Embeddings of Knowledge Bases
(2011)

Many Knowledge Bases (KBs) are now readily available and encompass colossal quantities of information thanks to either a long-term funding effort (e.g. WordNet, OpenCyc) or a collaborative process (e.g. Freebase, DBpedia). However, each of them is based on a different rigid symbolic framework which makes it hard to use their data in other systems. It is unfortunate because such rich structured knowledge might lead to a huge leap forward in many other areas of AI like natural language processing (word-sense disambiguation, natural language understanding, ...), vision (scene classification, image semantic annotation, ...) or collaborative filtering. In this paper, we present a learning process based on an innovative neural network architecture designed to embed any of these symbolic representations into a more flexible continuous vector space in which the original knowledge is kept and enhanced. These learnt embeddings would allow data from any KB to be easily used in recent machine learning methods for prediction and information retrieval. We illustrate our method on WordNet and Freebase and also present a way to adapt it to knowledge extraction from raw text.
Safety of Machine Learning Systems in Autonomous Driving
(2017)

Machine Learning, and in particular Deep Learning, are extremely capable tools for solving problems which are difficult, or intractable to tackle analytically. Application areas include pattern recognition, computer vision, speech and natural language processing. With the automotive industry aiming for increasing amount of automation in driving, the problems to solve become increasingly complex, which appeals to the use of supervised learning methods from Machine Learning and Deep Learning. With this approach, solutions to the problems are learned implicitly from training data, and inspecting their correctness is not possible directly. This presents concerns when the resulting systems are used to support safety-critical functions, as is the case with autonomous driving of automotive vehicles. This thesis studies the safety concerns related to learning systems within autonomous driving and applies a safety monitoring approach to a collision avoidance scenario. Experiments are performed using a simulated environment, with a deep learning system supporting perception for vehicle control, and a safety monitor for collision avoidance. The related operational situations and safety constraints are studied for an autonomous driving function, with potential faults in the learning system introduced and examined. Also, an example is considered for a measure that indicates trustworthiness of the learning system during operation.
A Broadband U-Slot Coupled Microstrip-to-Waveguide Transition
(2012)

A novel planar broadband microstrip-to-waveguide transition is proposed in this paper. The referred waveguide can be either rectangular waveguide or ridged waveguide. The transition consists of an open-circuited microstrip quarter-wavelength resonator and a resonant U-shaped slot on the upper broadside wall of a short-circuited waveguide. A physics-based equivalent-circuit model is also developed for interpreting the working mechanism and providing a coarse model for engineering design. The broadband transition can be regarded as a stacked two-pole resonator filter. Each coupling circuit can be approximately designed separately using the group-delay information at the center frequency. In addition to its broadband attribute, the transition is compact in size, vialess, and is highly compatible with planar circuits. These good features make the new transition very attractive for the system architecture where waveguide devices need to be surface mounted on a multilayered planer circuit. Two design examples are given to demonstrate the usefulness of the transition: one is a broadband ridged-waveguide bandpass filter and the other is a surface-mountable broadband low-temperature co-fired ceramic laminated waveguide cavity filter. Both filters are with the proposed transition for interfacing with microstrip lines, showing promising potentials in practical applications.
Optimized, Direct Sale of Privacy in Personal-Data Marketplaces
(2018)

Very recently, we are witnessing the emergence of a number of start-ups that enables individuals to sell their private data directly to brokers and businesses. While this new paradigm may shift the balance of power between individuals and companies that harvest data, it raises some practical, fundamental questions for users of these services: how they should decide which data must be vended and which data protected, and what a good deal is. In this work, we investigate a mechanism that aims at helping users address these questions. The investigated mechanism relies on a hard-privacy model and allows users to share partial or complete profile data with broker companies in exchange for an economic reward. The theoretical analysis of the trade-off between privacy and money posed by such mechanism is the object of this work. We adopt a generic measure of privacy although part of our analysis focuses on some important examples of Bregman divergences. We find a parametric solution to the problem of optimal exchange of privacy for money, and obtain a closed-form expression and characterize the trade-off between profile-disclosure risk and economic reward for several interesting cases.
Performance analysis of MapReduce programs on Hadoop cluster
(2012)

This paper discusses various MapReduce applications like pi, wordcount, grep, Terasort. We have shown experimental results of these applications on a Hadoop cluster. In this paper, performance of above application has been shown with respect to execution time and number of nodes. We find that as the number of nodes increases the execution time decreases. This paper is basically a research study of above MapReduce applications.
Ten key considerations for the successful implementation and adoption of large-scale health information technology.
(2013)

The implementation of health information technology interventions is at the forefront of most policy agendas internationally. However, such undertakings are often far from straightforward as they require complex strategic planning accompanying the systemic organizational changes associated with such programs. Building on our experiences of designing and evaluating the implementation of large-scale health information technology interventions in the USA and the UK, we highlight key lessons learned in the hope of informing the on-going international efforts of policymakers, health directorates, healthcare management, and senior clinicians.
Classification of Age Groups Based on Facial Features
(2002)

An age group classification system for gray-scale facial images is proposed in this paper. Four age groups, including babies, young adults, middle-aged adults, and old adults, are used in the classification system. The process of the system is divided into three phases: location, feature extraction, and age classification. Based on the symmetry of human faces and the variation of gray levels, the positions of eyes, noses, and mouths could be located by applying the Sobel edge operator and region labeling. Two geometric features and three wrinkle features from a facial image are then obtained. Finally, two back-propagation neural networks are constructed for classification. The first one employs the geometric features to distinguish whether the facial image is a baby. If it is not, then the second network uses the wrinkle features to classify the image into one of three adult groups. The proposed system is experimented with 230 facial images on a Pentium II 350 processor with 128 MB RAM. One half of the images are used for training and the other half for test. It takes 0.235 second to classify an image on an average. The identification rate achieves 90.52% for the training images and 81.58% for the test images, which is roughly close to human’s subjective justification.
Observing Tutorial Dialogues Collaboratively: Insights About Human Tutoring Effectiveness From Vicarious Learning
(2008)

The goals of this study are to evaluate a relatively novel learning environment, as well as to seek greater understanding of why human tutoring is so effective. This alternative learning environment consists of pairs of students collaboratively observing a videotape of another student being tutored. Comparing this collaboratively observing environment to four other instructional methods-one-on-one human tutoring, observing tutoring individually, collaborating without observing, and studying alone-the results showed that students learned to solve physics problems just as effectively from observing tutoring collaboratively as the tutees who were being tutored individually. We explain the effectiveness of this learning environment by postulating that such a situation encourages learners to become active and constructive observers through interactions with a peer. In essence, collaboratively observing combines the benefit of tutoring with the benefit of collaborating. The learning outcomes of the tutees and the collaborative observers, along with the tutoring dialogues, were used to further evaluate three hypotheses explaining why human tutoring is an effective learning method. Detailed analyses of the protocols at several grain sizes suggest that tutoring is effective when tutees are independently or jointly constructing knowledge: with the tutor, but not when the tutor independently conveys knowledge.
Asynchronous Large-Scale Graph Processing Made Easy
(2013)

Scaling large iterative graph processing applications through parallel computing is a very important problem. Several graph processing frameworks have been proposed that insulate developers from low-level details of parallel programming. Most of these frameworks are based on the bulk synchronous parallel (BSP) model in order to simplify application development. However, in the BSP model, vertices are processed in fixed rounds, which often leads to slow convergence. Asynchronous executions can significantly accelerate convergence by intelligently ordering vertex updates and incorporating the most recent updates. Unfortunately, asynchronous models do not provide the programming simplicity and scalability advantages of the BSP model. In this paper, we combine the easy programmability of the BSP model with the high performance of asynchronous execution. We have designed GRACE, a new graph programming platform that separates application logic from execution policies. GRACE provides a synchronous iterative graph programming model for users to easily implement, test, and debug their applications. It also contains a carefully designed and implemented parallel execution engine for both synchronous and user-specified built-in asynchronous execution policies. Our experiments show that asynchronous execution in GRACE can yield convergence rates comparable to fully asynchronous executions, while still achieving the near-linear scalability of a synchronous BSP system.
Classification with Fairness Constraints: A Meta-Algorithm with Provable Guarantees
(2019)

Developing classification algorithms that are fair with respect to sensitive attributes of the data is an important problem due to the increased deployment of classification algorithms in societal contexts. Several recent works have focused on studying classification with respect to specific fairness metrics, modeled the corresponding fair classification problem as constrained optimization problems, and developed tailored algorithms to solve them. Despite this, there still remain important metrics for which there are no fair classifiers with theoretical guarantees; primarily because the resulting optimization problem is non-convex. The main contribution of this paper is a meta-algorithm for classification that can take as input a general class of fairness constraints with respect to multiple non-disjoint and multi-valued sensitive attributes, and which comes with provable guarantees. In particular, our algorithm can handle non-convex "linear fractional" constraints (which includes fairness constraints such as predictive parity) for which no prior algorithm was known. Key to our results is an algorithm for a family of classification problems with convex constraints along with a reduction from classification problems with linear fractional constraints to this family. Empirically, we observe that our algorithm is fast, can achieve near-perfect fairness with respect to various fairness metrics, and the loss in accuracy due to the imposed fairness constraints is often small.
Understanding Instructions on Large Scale for Human-Robot Interaction
(2014)

Correctly interpreting human instructions is the first step to human-robot interaction. Previous approaches to semantically parsing the instructions relied on large numbers of training examples with annotation to widely cover all words in a domain. Annotating large enough instructions with semantic forms needs exhaustive engineering efforts. Hence, we propose propagating the semantic lexicon to learn a semantic parser from limited annotations, whereas the parser still has the ability of interpreting instructions on a large scale. We assume that the semantically-close words have the same semantic form based on the fact that human usually uses different words to refer to a same object or task. Our approach softly maps the unobserved words/phrases to the semantic forms learned from the annotated copurs through a metric for knowledge-based lexical similarity. Experiments on the collected instructions showed that the semantic parser learned with lexicon propagation outperformed the baseline. Our approach provides an opportunity for the robots to understand the human instructions on a large scale.
Evolutionary Bayesian Rose Trees
(2015)

We present an evolutionary multi-branch tree clustering method to model hierarchical topics and their evolutionary patterns overtime. The method builds evolutionary trees in a Bayesian online filtering framework. The tree construction is formulated as an online posterior estimation problem, which well balances both the fitness of the current tree and the smoothness between trees. The state-of-the-art multi-branch clustering method, Bayesian rose trees, is employed to generate a topic tree with a high fitness value. A constraint model is also introduced to preserve the smoothness between trees. A set of comprehensive experiments on real world news data demonstrates that the proposed method better incorporates historical tree information and is more efficient and effective than the traditional evolutionary hierarchical clustering algorithm. In contrast to our previous method [31], we implement two additional baseline algorithms to compare them with our algorithm. We also evaluate the performance of the clustering algorithm based on multiple constraint trees. Furthermore, two case studies are conducted to demonstrate the effectiveness and usefulness of our algorithm in helping users understand the major hierarchical topic evolutionary patterns in text data.
Capacitive Touch Systems With Styli for Touch Sensors: A Review
(2018)

This paper presents the latest progress in development and applications of the capacitive touch systems (CTSs) with styli. The CTSs, which include the touch sensor, analog front-end (AFE) integrated circuit (IC), and micro-controller unit, are reviewed along with the passive and active styli. The architecture of the CTS is explained first, followed by an exploration of the touch sensors: (1) types of touch sensors, such as in-cell, on-cell, and add-on types according to the size and material, (2) AFE IC with the driving and sensing methods, and (3) passive and active styli for the CTS. Finally, the future perspectives of the CTS are given from the viewpoint of the technical developments.
A Multi-Scale CNN and Curriculum Learning Strategy for Mammogram Classification
(2017)

Screening mammography is an important front-line tool for the early detection of breast cancer, and some 39 million exams are conducted each year in the United States alone. Here, we describe a multi-scale convolutional neural network (CNN) trained with a curriculum learning strategy that achieves high levels of accuracy in classifying mammograms. Specifically, we first train CNN-based patch classifiers on segmentation masks of lesions in mammograms, and then use the learned features to initialize a scanning-based model that renders a decision on the whole image, trained end-to-end on outcome data. We demonstrate that our approach effectively handles the “needle in a haystack” nature of full-image mammogram classification, achieving 0.92 AUROC on the DDSM dataset.
QRPp1-4: Characterizing Quality of Time and Topology in a Time Synchronization Network
(2006)

As Internet computing gains speed, complexity, and becomes ubiquitous, the need for precise and accurate time synchronization increases. In this paper, we present a characterization of a clock synchronization network managed by Network Time Protocol (NTP), composed by thousands of nodes, including hundreds of Stratum 1 servers, based on data collected recently by a robot. NTP is the most common protocol for time synchronization in the Internet. Many aspects that define the quality of timekeeping are analyzed, as well as topological characteristics of the network. The results are compared to previous characterizations of the NTP network, showing the evolution of clock synchronization in the last fifteen years.
Exact Inference Techniques for the Dynamic Analysis of Bayesian Attack Graphs
(2015)

Attack graphs are a powerful tool for security risk assessment by analysing network vulnerabilities and the paths attackers can use to compromise valuable network resources. The uncertainty about the attacker’s behaviour and capabilities make Bayesian networks suitable to model attack graphs to perform static and dynamic analysis. Previous approaches have focused on the formalization of traditional attack graphs into a Bayesian model rather than proposing mechanisms for their analysis. In this paper we propose to use efficient algorithms to make exact inference in Bayesian attack graphs, enabling the static and dynamic network risk assessments. To support the validity of our proposed approach we have performed an extensive experimental evaluation on synthetic Bayesian attack graphs with different topologies, showing the computational advantages in terms of time and memory use of the proposed techniques when compared to existing approaches.
Daily Fluctuations in Smartphone Use, Psychological Detachment, and Work Engagement: The Role of Workplace Telepressure
(2018)

Today's work environment is shaped by the electronic age. Smartphones are important tools that allow employees to work anywhere and anytime. The aim of this diary study was to examine daily smartphone use after and during work and their association with psychological detachment (in the home domain) and work engagement (in the work domain), respectively. We explored whether workplace telepressure, which is a strong urge to respond to work-related messages and a preoccupation with quick response times, promotes smartphone use. Furthermore, we hypothesized that employees experiencing high workplace telepressure would have more trouble letting go of the workday during the evening and feel less engaged during their workday to the extent that they use their smartphone more intensively across domains. A total of 116 employees using their smartphones for work-related purposes completed diary questionnaires on five workdays (N = 476 data points) assessing their work-related smartphone use, psychological detachment after work, and engagement during work. Workplace telepressure was measured as a between-individual variable and only assessed at the beginning of the study, as well as relevant control variables such as participants' workload and segmentation preference (a preference for work and home domains to be as segmented as possible). Multilevel path analyses revealed that work-related smartphone use after work was negatively related to psychological detachment irrespective of employees' experienced workplace telepressure, and daily smartphone use during work was unrelated to work engagement. Supporting our hypothesis, employees who reported high telepressure experienced less work engagement on days that they used their smartphone more intensively during work. Altogether, intensive smartphone use after work hampers employees' psychological detachment, whereas intensive smartphone use during work undermines their work engagement only when employees experience high workplace telepressure as well. Theoretical and practical implications of these findings are discussed.
Associative Embedding: End-to-End Learning for Joint Detection and Grouping
(2017)

We introduce associative embedding, a novel method for supervising convolutional neural networks for the task of detection and grouping. A number of computer vision problems can be framed in this manner including multi-person pose estimation, instance segmentation, and multi-object tracking. Usually the grouping of detections is achieved with multi-stage pipelines, instead we propose an approach that teaches a network to simultaneously output detections and group assignments. This technique can be easily integrated into any state-of-the-art network architecture that produces pixel-wise predictions. We show how to apply this method to multi-person pose estimation and report state-of-the-art performance on the MPII and MS-COCO datasets.